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In the combinatorial recommender systems, multiple items are fed to the user at one time in the result page, where the correlations among the items have impact on the user behavior.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl · 2001
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Improving recommendation lists through topic diversification
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen · 2005
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Improving diversity in ranking using absorbing random walks
Xiaojin Zhu, Andrew Goldberg, Jurgen Van Gael, and David Andrzejewski · 2007
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An experimental comparison of click position-bias models
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey · 2008
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A dynamic bayesian network click model for web search ranking
Olivier Chapelle and Ya Zhang · 2009
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Ranking with submodular valuations
Yossi Azar and Iftah Gamzu · 2011
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Evaluating recommendation systems
Guy Shani and Asela Gunawardana · 2011
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Linear submodular bandits and their application to diversified retrieval
Yisong Yue and Carlos Guestrin · 2011
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The ubiquity of model-based reinforcement learning
Bradley B Doll, Dylan A Simon, and Nathaniel D Daw · 2012
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Determinantal point processes for machine learning
Alex Kulesza and Ben Taskar · 2012
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Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar, et al · 2012
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Robust models of mouse movement on dynamic web search results pages
Fernando Diaz, Ryen White, Georg Buscher, and Dan Liebling · 2013
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel Rahman Mohamed, and Geoffrey Hinton · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
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Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
Pedro G Campos, Fernando Díez, and Iván Cantador · 2014
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Context-aware recommender systems
Gediminas Adomavicius and Alexander Tuzhilin · 2015
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Click models for web search
Aleksandr Chuklin, Ilya Markov, and Maarten de Rijke · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Doubly robust off-policy value evaluation for reinforcement learning
Nan Jiang and Lihong Li · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard H. Hovy · 2016
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio · 2017
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Learning combinatorial optimization algorithms over graphs
Elias B. Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Graph-based clustering and ranking for diversified image search
Yan Yan, Gaowen Liu, Sen Wang, Jian Zhang, and Kai Zheng · 2017
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Counterfactual estimation and optimization of click metrics in search engines: A case study
Lihong Li, Shunbao Chen, Jim Kleban, and Ankur Gupta · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Incorporating non-sequential behavior into click models
Chao Wang, Yiqun Liu, Meng Wang, Ke Zhou, Jian-yun Nie, and Shaoping Ma · 2015
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Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
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Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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Fast matrix factorization for online recommendation with implicit feedback
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua · 2016
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Toward better interactions in recommender systems: cycling and serpentining approaches for top-n item lists
Qian Zhao, Gediminas Adomavicius, F Maxwell Harper, Martijn Willemsen, and Joseph A Konstan · 2017
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From greedy selection to exploratory decision-making: Diverse ranking with policy-value networks
Yue Feng, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2018
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Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and Anxiang Zeng · 2018
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Practical diversified recommendations on youtube with determinantal point processes
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H Chi, and Jennifer Gillenwater · 2018
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Deep reinforcement learning for page-wise recommendations
Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang · 2018
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Drn: A deep reinforcement learning framework for news recommendation
Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li · 2018
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